Data Description:

You are provided with a training set and a test set of images of 3 classes. Each image has a filename that is its unique id. The dataset comprises 3 classes: COVID-19, Viral Pneumonia, and Normal. The goal of the projectis to create a classifier capable of determining the class of the X-ray image.

Dataset:

The project isinspiredfrom a dataset from Kaggle.Note: For project purposes, the dataset is modified according to the learning requirements.You are provided with the following files:

Context:

Reading the Dataset

Overview of the dataset

We see that most of the labels in the training set are of covid patients followed by Viral Pneumonia and normal having the same amonut of patients

Mean Images for all the labels

Data Preprocessing

Gaussian Blurring

Normalizing images

Making the data compatible

We see that the size of y_train is (251,12).Covid is the most common label forllowed by Viral pneumonia and normal

We see that the size of y_train is (66,12).Covid is the most common label forllowed by Viral pneumonia and normal

Splitting the training set to training and validaton set

Reshaping the train,test and validation set

Building the model

We see that the model is generalizing well to the validation set.

We have got a good accuracy on the testing set of 86.36% and the model has generalized well with the validation set

Visualizing the Predictions

We see that we have 9 entries that have been classified incorrectly however the majority have been classified correctly

Reducing Overfitting

The model isnt generalising well with the validation set

The accuracy remains the same in the second model

Conclusion: